Affective Computing for Social Good : Enhancing Well-being, Empathy, and Equity / / edited by Muskan Garg, Rajesh Shardanand Prasad
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| Autore: |
Garg Muskan
|
| Titolo: |
Affective Computing for Social Good : Enhancing Well-being, Empathy, and Equity / / edited by Muskan Garg, Rajesh Shardanand Prasad
|
| Pubblicazione: | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Edizione: | 1st ed. 2024. |
| Descrizione fisica: | 1 online resource (274 pages) |
| Disciplina: | 004.019 |
| Soggetto topico: | Artificial intelligence |
| Social sciences - Data processing | |
| Machine learning | |
| Emotions | |
| Natural language processing (Computer science) | |
| Artificial Intelligence | |
| Computer Application in Social and Behavioral Sciences | |
| Machine Learning | |
| Emotion | |
| Natural Language Processing (NLP) | |
| Altri autori: |
PrasadRajesh Shardanand
|
| Nota di contenuto: | Chapter 1 The Science of Emotion: Decoding and Analysis of Human Emotional Landscape -- Chapter 2 The Synergy of Clinical Psychology and Affective Computing: Advancements in Emotion Recognition and Therapy -- Chapter 3 Affective Computing in Mood Disorders: Beyond Conventional Diagnostic Tools to Modern Technologies -- Chapter 4 The Role of Affective Computing in Social Justice: Harnessing Equity and Inclusion -- Chapter 5 Exploring the Ethical Dimensions and Societal Consequences of Affective Computing -- Chapter 6 Natural Language Processing for Emotion Recognition and Analysis -- Chapter 7 From Data to Emotions: Affective Computing in Voice Emotion Detection -- Chapter 8 Visual Emotion Recognition Through Affective Computing -- Chapter 9 Affective Computing for Health Management via Recommender Systems: Exploring Challenges and Opportunities -- Chapter 10 Personalized Well-being Interventions (PWIs): A New Frontier in Mental Health -- Chapter 11 Enhancing Affective Computing in NLP through Data Augmentation: Strategies for Overcoming Limited Data Availability -- Chapter 12 Advancements in Multimodal Emotion Recognition: Integrating Facial Expressions and Physiological Signals -- Chapter 13 Ethical Considerations in Affective Computing -- Chapter 14 The Horizon of Consciousness for Affective Computing: Future Trends and Possibilities. |
| Sommario/riassunto: | Affective Computing for Social Good: Enhancing Well-being, Empathy, and Equity offers an insightful journey into the intricate realm of affective computing. It covers a spectrum of topics ranging from foundational theories and technologies to ethical considerations and future possibilities. Beginning with "Deciphering the Emotional Spectrum: Advances in Emotion Science and Analysis," it sets the stage by tracing the evolution of understanding human emotions. Subsequent chapters explore practical applications, such as integrating clinical psychology with affective computing for therapeutic progress and leveraging affective computing in diagnosing and managing mood disorders more efficiently. As the narrative unfolds, the book emphasizes the crucial role of affective computing in fostering social justice and equity. It underscores the need for developing inclusive algorithms and databases while addressing ethical challenges like privacy, consent, and the risk of emotional manipulation. These discussions emphasize the significance of ethical deployment and regulation. The book also covers the technical aspects and applications of affective computing, including natural language processing for emotion recognition and analysis, voice emotion detection, and visual emotion recognition. It extends to applications, such as the use of affective computing in health management via recommender systems and personalized well-being interventions in mental health care. Overall, this book will help readers gain a deeper understanding of the intersection between AI and human emotions, and how this technology can be used to create a more empathetic, compassionate, and socially responsible world. |
| Titolo autorizzato: | Affective Computing for Social Good |
| ISBN: | 3-031-63821-2 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910896188103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
The Springer Series in Applied Machine Learning, . 2520-1301